This invention pertains to apparatus for and methods of classifying patterns, and, more particularly, to the processing of signals for object recognition.
In many fields, it is desirable to extract information which identifies an object from signals which also contain extraneous information. For example, radar automatic target recognition (ATR) starts with a set of measurements on the target. This is shown diagrammatically in FIG. 1. A transmitter 10 sends a radar beam 20 toward a target 30. The return radar beam 40 is received by a receiver 50 which sends signals indicative of selected parameters of the return radar signal 40 to an automatic target recognition system 60. The transmitter 10 and the receiver 50 may be separate (bi-static) or collocated (monostatic).
In the automatic target recognition system 60 the signals from the receiver 50 are subjected to a Fourier transform and the magnitude of the transformed signals is detected to create a pattern space. The pattern space is usually of high dimensionality and therefore needs to be reduced to create a feature space. The reduction must be performed, however, without significant loss of identifying information.
To be more specific, wide bandwidth frequency agile processing yields high resolution profiles of a target. These profiles are made up of the return from radar scattering centers on the target. The profiles change with aspect angle since the scattering centers become visible or obscured as the aspect angle changes with respect to the radar line-of-sight. By transmitting with multiple polarized radiation, different profiles can be observed which reflect different slices of the target. For example, by transmitting both circular polarizations (Left Hand Circular (LHC) and Right Hand Circular (RHC)) and receiving both polarizations, a full scatter matrix may be developed as follows: ##EQU1## In the above expression, the first letter of each subscript refers to the received polarization and the second letter of each subscript refers to the transmitted polarization with L being LHC and R being RHC.
Each element of the scatter matrix is a high resolution range profile of the target. The LL and RR returns contain the "even" bounce type of scatter such as comes from a dihedral (2 sided reflector). The LR and RL returns contain the odd bounce scattering such as from a trihedral (3 sided reflector), a sphere, or a flat plat. For a monostatic radar (one antenna used for both transmission and reception) the odd bounce LR and RL profiles are the same, so only one is used.
The overall process used in conventional ATR is shown diagrammatically in FIG. 2. First, a return signal is acquired and a domain transform such as a Fast Fourier Transform (FFT) is performed in a domain transform unit 70. The domain transform may be performed by a suitably programmed general computing device. This creates a measurement space which is operated on by a magnitude detector 80. The output of the magnitude detector 80 is considered to be the pattern space.
FIG. 3 shows an example of a high resolution profile of an element of the polarimetric scatter matrix of a target at a 45 degree aspect angle. This is one of a set of profiles or signatures which together comprise the pattern space of a full polarimetric, high range resolution radar. When a target is placed on a turntable and rotated through 360 degrees of aspect angle, profiles such as that shown expand and contract over the radial projection of the length of the target-as the projected length and the number of dominant scatterers in the profile varies.
The signature in FIG. 3 constitutes an N-dimensional vector. There is one such N-dimensional vector for each of the scatter matrix elements of interest (LL, LR, and RR for monostatic operation). Furthermore, the profiles represent an N-dimensional random vector since they depend on a random aspect angle.
As can be seen in FIG. 3, the target does not occupy the full profile presented. The part of the profile not taken up by the target is considered noise or background clutter. In the case shown, the full profile is length 64 and the target only occupies a portion of this space at a 45 degree aspect angle.
The full 64 point profiles are the information about the target observable by the radar in the three polarization cuts. In pattern recognition, the pattern space, which is usually large (in this case 3.times.64), needs to be reduced to a reasonably-sized feature space that captures the bulk of the information presented.
As indicated by FIG. 2, the conventional method of developing the feature space from the pattern space uses a heuristic feature selector 90 which operates in accordance with a statistical analysis of the target signature in terms of extent of the target and where the dominant scatterers are located. A common method of selecting features, depicted in FIG. 4, involves choosing the centroid of the pattern as a reference and then choosing a range of values ("placing a window" around) the centroid. Then experiments are performed by varying the extent of the target extracted as features. Various combinations of targets and classifiers such as the pattern classifier 100 are trained and tested to select the best features. Heuristic judgment is involved in the final decision.
The process used can thus be summarized as follows. First, the pattern recognition system receives a set of data (in the example, a set of target signatures as shown in FIG. 3). Then, after a domain transform and a magnitude detection, the system finds a common reference by a centroiding technique. Then, using the results from the statistical analysis, the system places a window around the reference point enclosing a reduced feature set. The resulting set of features is then used for training and testing a classifier.
Current techniques thus rely on heuristic feature selection or require many features (high dimensionality). Those practices which rely on heuristic approaches for selecting features suffer from the disadvantage that the heuristic approach must be constantly changed at great cost as targets are added or background clutter environments change.
There thus remains a need to improve image recognition systems such as ATR by achieving faster and more robust performance, reducing hardware, cost and processing requirements, and improving reliability.